R Notebook

library(plotly)
library(tidyverse)
library(readr)
library(ggplot2)
library(dplyr)
library(gridExtra)
library(htmlwidgets)

data <- read.csv("high_elo_opening.csv")

columns_to_keep <- c(
  "opening_name",
  "side",
  "num_games",
  "ECO",
  "last_played_date",
  "avg_player",
  "perc_draw",
  "perc_white_win",
  "perc_black_win"
)

# Convert the "last_played_date" column to a date format
data$last_played_date <- as.Date(data$last_played_date)

# Create a new dataset with only the relevant columns
cleaned_data <- data %>%
  select(all_of(columns_to_keep))

ggplot(cleaned_data, aes(x = avg_player)) +
  geom_histogram(binwidth = 50, fill = "blue", color = "black") +
  labs(title = "Distribution of Average Player Ratings",
       x = "Average Player Rating",
       y = "Frequency")

# Filter for the top 10 most frequently played openings
top_10_openings <- cleaned_data %>%
  group_by(opening_name) %>%
  summarize(total_games = sum(num_games)) %>%
  arrange(desc(total_games)) %>%
  head(10)

# Filter the dataset to include only the top 10 openings
filtered_data <- cleaned_data %>%
  filter(opening_name %in% top_10_openings$opening_name)

# Calculate win rates for white, draw, and black for each opening
opening_win_rates <- filtered_data %>%
  group_by(opening_name) %>%
  summarize(
    perc_white_win = mean(perc_white_win),
    perc_draw = mean(perc_draw),
    perc_black_win = mean(perc_black_win)
  )

# Reshape the data for plotting
opening_win_rates_long <- opening_win_rates %>%
  pivot_longer(
    cols = c(perc_white_win, perc_draw, perc_black_win),
    names_to = "result",
    values_to = "percentage"
  )

# Create the stacked bar chart
ggplot(opening_win_rates_long, aes(x = reorder(opening_name, percentage), y = percentage, fill = result)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  scale_y_continuous(labels = scales::percent_format(scale = 1)) +
  labs(
    title = "Win Rates per Opening (Top 10 Most Popular Openings)",
    x = "Opening",
    y = "Percentage",
    fill = "Result"
  ) +
  theme(legend.position = "bottom")+
  scale_x_discrete(labels = function(x) str_wrap(x, width = 40))

# Filter data for player ratings less than 2000
data_below_2000 <- cleaned_data %>%
  filter(avg_player < 2000)

# Identify the top 10 most played openings for ratings below 2000
top_10_openings_below_2000 <- data_below_2000 %>%
  group_by(opening_name) %>%
  summarize(total_games = sum(num_games)) %>%
  arrange(desc(total_games)) %>%
  head(10)

# Calculate win rates for the top 10 openings in this category
win_rates_below_2000 <- data_below_2000 %>%
  filter(opening_name %in% top_10_openings_below_2000$opening_name) %>%
  group_by(opening_name) %>%
  summarize(
    perc_white_win = mean(perc_white_win),
    perc_draw = mean(perc_draw),
    perc_black_win = mean(perc_black_win)
  )

# Reshape the data for plotting
win_rates_below_2000_long <- win_rates_below_2000 %>%
  pivot_longer(
    cols = c(perc_white_win, perc_draw, perc_black_win),
    names_to = "result",
    values_to = "percentage"
  )

# Create a bar chart for player ratings < 2000
ggplot(win_rates_below_2000_long, aes(x = reorder(opening_name, percentage), y = percentage, fill = result)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  scale_y_continuous(labels = scales::percent_format(scale = 1)) +
  labs(
    title = "Win Rates per Opening (Player Ratings < 2000)",
    x = "Opening",
    y = "Percentage",
    fill = "Result"
  ) +
  theme(legend.position = "bottom")+
  scale_x_discrete(labels = function(x) str_wrap(x, width = 40))

# Filter data for player ratings between 2000 and 2200
data_2000_2200 <- cleaned_data %>%
  filter(avg_player >= 2000, avg_player < 2200)

# Identify the top 10 most played openings for ratings between 2000 and 2200
top_10_openings_2000_2200 <- data_2000_2200 %>%
  group_by(opening_name) %>%
  summarize(total_games = sum(num_games)) %>%
  arrange(desc(total_games)) %>%
  head(10)

# Calculate win rates for the top 10 openings in this category
win_rates_2000_2200 <- data_2000_2200 %>%
  filter(opening_name %in% top_10_openings_2000_2200$opening_name) %>%
  group_by(opening_name) %>%
  summarize(
    perc_white_win = mean(perc_white_win),
    perc_draw = mean(perc_draw),
    perc_black_win = mean(perc_black_win)
  )

# Reshape the data for plotting
win_rates_2000_2200_long <- win_rates_2000_2200 %>%
  pivot_longer(
    cols = c(perc_white_win, perc_draw, perc_black_win),
    names_to = "result",
    values_to = "percentage"
  )

# Create a bar chart for player ratings 2000-2200
ggplot(win_rates_2000_2200_long, aes(x = reorder(opening_name, percentage), y = percentage, fill = result)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  scale_y_continuous(labels = scales::percent_format(scale = 1)) +
  labs(
    title = "Win Rates per Opening (Player Ratings 2000-2200)",
    x = "Opening",
    y = "Percentage",
    fill = "Result"
  ) +
  theme(legend.position = "bottom")+
  scale_x_discrete(labels = function(x) str_wrap(x, width = 40))

# Filter data for player ratings 2200+
data_2200_plus <- cleaned_data %>%
  filter(avg_player >= 2200)

# Identify the top 10 most played openings for ratings 2200+
top_10_openings_2200_plus <- data_2200_plus %>%
  group_by(opening_name) %>%
  summarize(total_games = sum(num_games)) %>%
  arrange(desc(total_games)) %>%
  head(10)

# Calculate win rates for the top 10 openings in this category
win_rates_2200_plus <- data_2200_plus %>%
  filter(opening_name %in% top_10_openings_2200_plus$opening_name) %>%
  group_by(opening_name) %>%
  summarize(
    perc_white_win = mean(perc_white_win),
    perc_draw = mean(perc_draw),
    perc_black_win = mean(perc_black_win)
  )

# Reshape the data for plotting
win_rates_2200_plus_long <- win_rates_2200_plus %>%
  pivot_longer(
    cols = c(perc_white_win, perc_draw, perc_black_win),
    names_to = "result",
    values_to = "percentage"
  )

# Create a bar chart for player ratings 2200+
ggplot(win_rates_2200_plus_long, aes(x = reorder(opening_name, percentage), y = percentage, fill = result)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  scale_y_continuous(labels = scales::percent_format(scale = 1)) +
  labs(
    title = "Win Rates per Opening (Player Ratings 2200+)",
    x = "Opening",
    y = "Percentage",
    fill = "Result"
  ) +
  theme(legend.position = "bottom")+
  scale_x_discrete(labels = function(x) str_wrap(x, width = 40))

plot_ly(cleaned_data, x = ~avg_player, y = ~num_games, text = ~paste("Opening: ", opening_name, "<br>Win Rate: ", perc_white_win), type = "scatter", mode = "markers", name = "my_plot") %>%
  add_markers(color = ~perc_white_win, colors = "Viridis") %>%
  layout(
    title = "Interactive Opening Popularity vs. Player Ratings",
    xaxis = list(title = "Player Ratings (Average)"),
    yaxis = list(title = "Number of Games Played with Opening"),
    showlegend = FALSE
  )

---
title: "R Notebook"
output: html_notebook
---

Install and load packages
```{r}
library(plotly)
library(tidyverse)
library(readr)
library(ggplot2)
library(dplyr)
library(gridExtra)
library(htmlwidgets)
```

Read CSV files
```{r}
data <- read.csv("high_elo_opening.csv")
```

Clean the data file
```{r}
columns_to_keep <- c(
  "opening_name",
  "side",
  "num_games",
  "ECO",
  "last_played_date",
  "avg_player",
  "perc_draw",
  "perc_white_win",
  "perc_black_win"
)

# Convert the "last_played_date" column to a date format
data$last_played_date <- as.Date(data$last_played_date)

# Create a new dataset with only the relevant columns
cleaned_data <- data %>%
  select(all_of(columns_to_keep))
```

# Exploratory Data Analysis (EDA)
```{r}
ggplot(cleaned_data, aes(x = avg_player)) +
  geom_histogram(binwidth = 50, fill = "blue", color = "black") +
  labs(title = "Distribution of Average Player Ratings",
       x = "Average Player Rating",
       y = "Frequency")
```

## Analyze Opening Popularity
```{r}
# Filter for the top 10 most frequently played openings
top_10_openings <- cleaned_data %>%
  group_by(opening_name) %>%
  summarize(total_games = sum(num_games)) %>%
  arrange(desc(total_games)) %>%
  head(10)

# Filter the dataset to include only the top 10 openings
filtered_data <- cleaned_data %>%
  filter(opening_name %in% top_10_openings$opening_name)

# Calculate win rates for white, draw, and black for each opening
opening_win_rates <- filtered_data %>%
  group_by(opening_name) %>%
  summarize(
    perc_white_win = mean(perc_white_win),
    perc_draw = mean(perc_draw),
    perc_black_win = mean(perc_black_win)
  )

# Reshape the data for plotting
opening_win_rates_long <- opening_win_rates %>%
  pivot_longer(
    cols = c(perc_white_win, perc_draw, perc_black_win),
    names_to = "result",
    values_to = "percentage"
  )

# Create the stacked bar chart
ggplot(opening_win_rates_long, aes(x = reorder(opening_name, percentage), y = percentage, fill = result)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  scale_y_continuous(labels = scales::percent_format(scale = 1)) +
  labs(
    title = "Win Rates per Opening (Top 10 Most Popular Openings)",
    x = "Opening",
    y = "Percentage",
    fill = "Result"
  ) +
  theme(legend.position = "bottom")+
  scale_x_discrete(labels = function(x) str_wrap(x, width = 40))
```

## Player Rating Categories
### Rating < 2000
```{r}
# Filter data for player ratings less than 2000
data_below_2000 <- cleaned_data %>%
  filter(avg_player < 2000)

# Identify the top 10 most played openings for ratings below 2000
top_10_openings_below_2000 <- data_below_2000 %>%
  group_by(opening_name) %>%
  summarize(total_games = sum(num_games)) %>%
  arrange(desc(total_games)) %>%
  head(10)

# Calculate win rates for the top 10 openings in this category
win_rates_below_2000 <- data_below_2000 %>%
  filter(opening_name %in% top_10_openings_below_2000$opening_name) %>%
  group_by(opening_name) %>%
  summarize(
    perc_white_win = mean(perc_white_win),
    perc_draw = mean(perc_draw),
    perc_black_win = mean(perc_black_win)
  )

# Reshape the data for plotting
win_rates_below_2000_long <- win_rates_below_2000 %>%
  pivot_longer(
    cols = c(perc_white_win, perc_draw, perc_black_win),
    names_to = "result",
    values_to = "percentage"
  )

# Create a bar chart for player ratings < 2000
ggplot(win_rates_below_2000_long, aes(x = reorder(opening_name, percentage), y = percentage, fill = result)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  scale_y_continuous(labels = scales::percent_format(scale = 1)) +
  labs(
    title = "Win Rates per Opening (Player Ratings < 2000)",
    x = "Opening",
    y = "Percentage",
    fill = "Result"
  ) +
  theme(legend.position = "bottom")+
  scale_x_discrete(labels = function(x) str_wrap(x, width = 40))
```

### Rating 2000-2200
```{r}
# Filter data for player ratings between 2000 and 2200
data_2000_2200 <- cleaned_data %>%
  filter(avg_player >= 2000, avg_player < 2200)

# Identify the top 10 most played openings for ratings between 2000 and 2200
top_10_openings_2000_2200 <- data_2000_2200 %>%
  group_by(opening_name) %>%
  summarize(total_games = sum(num_games)) %>%
  arrange(desc(total_games)) %>%
  head(10)

# Calculate win rates for the top 10 openings in this category
win_rates_2000_2200 <- data_2000_2200 %>%
  filter(opening_name %in% top_10_openings_2000_2200$opening_name) %>%
  group_by(opening_name) %>%
  summarize(
    perc_white_win = mean(perc_white_win),
    perc_draw = mean(perc_draw),
    perc_black_win = mean(perc_black_win)
  )

# Reshape the data for plotting
win_rates_2000_2200_long <- win_rates_2000_2200 %>%
  pivot_longer(
    cols = c(perc_white_win, perc_draw, perc_black_win),
    names_to = "result",
    values_to = "percentage"
  )

# Create a bar chart for player ratings 2000-2200
ggplot(win_rates_2000_2200_long, aes(x = reorder(opening_name, percentage), y = percentage, fill = result)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  scale_y_continuous(labels = scales::percent_format(scale = 1)) +
  labs(
    title = "Win Rates per Opening (Player Ratings 2000-2200)",
    x = "Opening",
    y = "Percentage",
    fill = "Result"
  ) +
  theme(legend.position = "bottom")+
  scale_x_discrete(labels = function(x) str_wrap(x, width = 40))
```

### Rating 2200+
```{r}
# Filter data for player ratings 2200+
data_2200_plus <- cleaned_data %>%
  filter(avg_player >= 2200)

# Identify the top 10 most played openings for ratings 2200+
top_10_openings_2200_plus <- data_2200_plus %>%
  group_by(opening_name) %>%
  summarize(total_games = sum(num_games)) %>%
  arrange(desc(total_games)) %>%
  head(10)

# Calculate win rates for the top 10 openings in this category
win_rates_2200_plus <- data_2200_plus %>%
  filter(opening_name %in% top_10_openings_2200_plus$opening_name) %>%
  group_by(opening_name) %>%
  summarize(
    perc_white_win = mean(perc_white_win),
    perc_draw = mean(perc_draw),
    perc_black_win = mean(perc_black_win)
  )

# Reshape the data for plotting
win_rates_2200_plus_long <- win_rates_2200_plus %>%
  pivot_longer(
    cols = c(perc_white_win, perc_draw, perc_black_win),
    names_to = "result",
    values_to = "percentage"
  )

# Create a bar chart for player ratings 2200+
ggplot(win_rates_2200_plus_long, aes(x = reorder(opening_name, percentage), y = percentage, fill = result)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  scale_y_continuous(labels = scales::percent_format(scale = 1)) +
  labs(
    title = "Win Rates per Opening (Player Ratings 2200+)",
    x = "Opening",
    y = "Percentage",
    fill = "Result"
  ) +
  theme(legend.position = "bottom")+
  scale_x_discrete(labels = function(x) str_wrap(x, width = 40))
```

## Interactive Scatterplot
```{r, fig.width = 10}
plot_ly(cleaned_data, x = ~avg_player, y = ~num_games, text = ~paste("Opening: ", opening_name, "<br>Win Rate: ", perc_white_win), type = "scatter", mode = "markers", name = "my_plot") %>%
  add_markers(color = ~perc_white_win, colors = "Viridis") %>%
  layout(
    title = "Interactive Opening Popularity vs. Player Ratings",
    xaxis = list(title = "Player Ratings (Average)"),
    yaxis = list(title = "Number of Games Played with Opening"),
    showlegend = FALSE
  )
```